PUMA560 Robot Manipulator Research Articles

MNTZNN for Solving Hybrid Double-Deck Dynamic Nonlinear Equation System Applied to Robot Manipulator Control.

Compared with conventional dynamic nonlinear equation systems, a hybrid double-deck dynamic nonlinear equation system (H3DNES) not only has multiple layers describing more different tasks in practice, but also has a hybrid nonlinear structure of solution and its derivative describing their nonlinear constraints. Its characteristics lead to the ability to describe more complicated problems involving multiple constraints, and strong nonlinear and dynamic features, such as robot manipulator tracking control. Besides, noises are inevitable in practice and thus strong robustness of models solving H3DNES is also necessary. In this work, a multilayered noise-tolerant zeroing neural network (MNTZNN) model is proposed for solving H3DNES. MNTZNN model has strong robustness and it solves H3DNES successfully even when noises exist in both the two layers of H3DNES. In order to develop the MNTZNN model, a new zeroing neural network (ZNN) design formula is proposed. It not only enables equations with respect to solutions to become equations with respect to the second-order derivatives of solutions but also makes the corresponding model have strong robustness. The robustness of the MNTZNN model is proved when parameters in the model satisfy a loose constraint and the error bounds are programmable via setting appropriate parameter values. Finally, the MNTZNN model is applied to the tracking control of the six-link planar robot manipulator and PUMA560 robot manipulator with hybrid nonlinear constraints of joint angle and velocity.

Adaptive sliding mode control for robotic manipulators with backlash

This paper provides the consequences for trajectory tracking of uncertain n-DOF robotic manipulators in the presence of external disturbances and backlash. The control strategy consists of decoupling system based on inverse dynamics, sliding mode controller, and optimal controller. The tendency of robotic manipulators with gear backlash is to function in two unique phases: contact phase or backlash phase. The sliding mode controller works in the contact phase, after the decoupling of the manipulator, a adaptive second-order sliding mode controller is designed to guarantee system achieve desirable tracking precision and robust stability. The optimal controller works in the backlash phase, which makes the motor quickly traverse the backlash gap and re-contact the joints without collision. Finally, the proposal is verified in simulation using a model of a PUMA560 robot manipulator identified from real data.

English

This paper presents trajectory tracking based on a sliding mode controller for the Puma robot manipulator. Puma is most commonly used in industries; it has great flexibility compared to other manipulators like SCARA, which decreases its precision. In order to increase the precision Sliding mode controller based on the Lyapunov stability approach is used in this work. The first motion control block for the sliding mode Controller is designed and link it with the Puma Robot manipulator in MATLAB Simulink. The Experiment Results for the effectiveness of this method are verified.

A Novel High-Speed Third-Order Sliding Mode Observer for Fault-Tolerant Control Problem of Robot Manipulators

In this paper, a novel fault-tolerant control tactic for robot manipulator systems using only position measurements is proposed. The proposed algorithm is constructed based on a combination of a nonsingular fast terminal sliding mode control (NFTSMC) and a novel high-speed third-order sliding mode observer (TOSMO). In the first step, the high-speed TOSMO is proposed for the first time to approximate both the system velocity and the lumped unknown input with a faster convergence time compared to the TOSMO. The faster convergence speed is obtained thanks to the linear characteristic of the added elements. In the second step, the NFTSMC is designed based on a nonsingular fast terminal sliding (NFTS) surface and the information obtained from the proposed high-speed TOSMO. Thanks to the combination, the proposed controller–observer tactic provides excellent features, such as a fast convergence time, high tracking precision, chattering phenomenon reduction, robustness against the effects of the lumped unknown input and velocity requirement elimination. Especially, the proposed observer does not only improve the convergence speed of the estimated signals, but also increases the system dynamic response. The system’s finite-time stability is proved using the Lyapunov theory. Finally, to validate the efficiency of the proposed strategy, simulations on a PUMA560 robot manipulator are performed.

Unified Model Solving Nine Types of Time-Varying Problems in the Frame of Zeroing Neural Network.

Many time-varying problems have been solved using the zeroing neural network proposed by Zhang et al. In this article, nine types of time-varying problems, namely time-varying nonlinear equation system, time-varying linear equation system, time-varying convex nonlinear optimization under linear equalities, unconstrained time-varying convex nonlinear optimization, time-varying convex quadratic programming under linear equalities, unconstrained time-varying convex quadratic programming, time-varying nonlinear inequality system, time-varying linear inequality system, and time-varying division, are investigated to better understand the essence of zeroing neutral network. Discrete-form time-varying problems are studied by considering the nature of unknown future and the requirement of real-time computation for time-varying problems. A unified model is proposed in the frame of zeroing neural network to uniformly solve these time-varying problems on the basis of their connections and a newly developed discretization formula. Theoretical analyses and numerical experiments, including the tracking control of PUMA560 robot manipulator, verify the effectiveness and precision of the proposed unified model.

Design Stable Controller for PUMA 560 Robot with PID and Sliding Mode Controller Based on PSO Algorithm

The most significant challenge facing the researcher in the field of robotics is to control the robot manipulator with appropriate overall performance. This paper focuses mainly on the novel Intelligent Particle Swarm Optimization (PSO) algorithm that was used for optimizing and tuning the gain of conventional Proportional Integral Derivative (PID), and improve the parameters of dynamic design in Sliding Mode Control (SMC), which is considered a strong nonlinear controller for controlling highly nonlinear systems, particularly for multi-degree serial link robot manipulator. Additional modified Integral Sliding Mode Controller (ISMC) was implemented to the design of dynamic system with high control theory of sliding mode controller. Intelligent Particle Swarm Optimization (PSO) algorithm was introduced for developing the nonlinear controller. The algorithm demonstrates superior performance in determining the appropriate gains and parameters value in harmony with robot scheme dynamic layout in order to achieve suitable and stable nonlinear controller, besides reduce the chattering phenomenon. PUMA robot manipulator that was used as study case in this work, shows perfect result in step response, with acceptable steady state, and overshoot, besides, eliminating the disadvantage of chattering in conventional SMC. Matlab / Simulink presents to increase the speed of matrix calculation in forward, inverse kinematics and dynamic model of manipulator. Comparison was made between the proposed method with existing methods. Result shows that integral sliding mode with PSO (ISMC/PSO) gave best result for stable step response, minimum mean square error with best objective function, and stable torque.

Imperialist Competitive Algorithm Optimised Adaptive Neuro Fuzzy Controller for Hybrid Force Position Control of an Industrial Robot Manipulator: A Comparative Study

Due to the nonlinear nature of the dynamics of a robot manipulator, controlling the robot meticulously is a challenging issue for control engineers. The key purpose of this paper is to provide an accurate intelligent method for refining the functionality of orthodox PID controller in the problem of force/position control of a robot manipulator with unspecified robot dynamics during external disturbances. A grouping of imperialist competitive algorithm (ICA) and adaptive neuro fuzzy logic is applied for the tuning of PID parameters. This, therefore, forms an intelligent structure, adaptive neuro fuzzy inference system with proportional derivative plus integral (ANFISPD + I) controller, which is more precise in definite and indefinite circumstances. To show the efficiency of the proposed method, this algorithm is applied to solve constrained dynamic force/position control problem of PUMA robot manipulator. The simulated results are compared to those achieved from other evolutionary techniques such as Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO). The simulation results exhibit that ICA-based ANFISPD + I outperforms the other evolutionary techniques. Highlights An ICA-ANFISPD + I-based hybrid force/position controller has been proposed. Easy to implement. Works well in the case of disturbances. Actuator Dynamics has been considered. External disturbances have been considered. Robot dynamics are unknown.

Trajectory tracking control based on non-singular fractional derivatives for the PUMA 560 robot arm

In this paper, a novel hybrid fractional-order control strategy for the PUMA-560 robot manipulator is developed and presented, which combines the derivative of Caputo–Fabrizio and the integral of Atangana–Baleanu, both in the Caputo sense. The fractional-order dynamic model of the system (FODM) is also considered which consists of two models, the robot manipulator model, and the model of the induction motors which are the actuators that drive their joints. The fractional model of the manipulator is obtained using the Euler–Lagrange formulation. On the other hand, for controlling each one of the induction motors, fractional-order controllers ${{\mathop{\mathrm{PI}}\nolimits } ^{\vartheta }}$ based on Atangana–Baleanu in the Caputo sense integral were developed. And for the trajectory tracking control, fractional-order controllers ${{\mathop{\mathrm{PD}}\nolimits } ^{\xi }}$ were developed based on the fractional derivative of Caputo–Fabrizio in the Caputo sense. Also, ordinary PI and PD controllers were developed for the PUMA robot control to compare their performance with the fractional-order controllers. The results obtained demonstrated that the fractional-order controllers have a better capability for tracking trajectory tasks than the integer-order controllers, even when changes of the desired trajectory and external disturbances are considered. Additionally, an end-effector trajectory tracking task for manufacturing applications is also considered. All numerical simulations were performed by using the same orders and gains, demonstrating that the proposed fractional-order ${{\mathop{\mathrm{PI}}\nolimits } ^{\vartheta }}$ and ${{\mathop{\mathrm{PD}}\nolimits } ^{\xi }}$ controllers are robust, under different operating conditions, for tracking trajectory tasks. The fractional-order controllers and the integer-order controllers were tuned applying the cuckoo search optimization algorithm where the root-mean-square error (RMSE) was chosen as the cost function to minimize.

Discrete-time nonlinear optimization via zeroing neural dynamics based on explicit linear multi-step methods for tracking control of robot manipulators

In this paper, discrete-time nonlinear optimization (DTNO) for tracking control of robot manipulators is investigated. By utilizing zeroing neural dynamics (ZND) method, a continuous-time ZND (CTZND) model is first proposed for solving the corresponding continuous-time nonlinear optimization (CTNO) problem. Afterwards, three different explicit linear multi-step methods (i.e., explicit linear 3-step, 2-step, and 1-step methods) are respectively presented and investigated. To solve such a DTNO problem, the explicit linear 3-step method is adopted to combine with the CTZND model, and hence a 3-step discrete-time ZND (DTZND) model is proposed. For comparison purposes, 2-step and 1-step DTZND models are also developed. Besides, theoretical analyses indicate the validity and superiority of the proposed 3-step DTZND model. Finally, the numerical experimental results based on a 2-joint robot manipulator and a PUMA560 robot manipulator further verify that the proposed 3-step DTZND model is much superior to the other two DTZND models.

An SVM-Based Neural Adaptive Variable Structure Observer for Fault Diagnosis and Fault-Tolerant Control of a Robot Manipulator

A robot manipulator is a multi-degree-of-freedom and nonlinear system that is used in various applications, including the medical area and automotive industries. Uncertain conditions in which a robot manipulator operates, as well as its nonlinearities, represent challenges for fault diagnosis and fault-tolerant control (FDC) that are addressed through the proposed FDC technique. A machine-learning-based neural adaptive, high-order, variable structure observer for fault diagnosis (FD) and adaptive, modern, fuzzy, backstepping, variable structure control for use in a fault-tolerant control (FC) algorithm, are proposed in this paper. In the first stage, a variable structure observer is proposed as an FD technique for the robot manipulator. The chattering phenomenon associated with the variable structure observer(VSO) is solved using a high-order variable structure observer. Then, the dynamic behavior estimation performance in the high-order variable structure observer is improved by incorporating a neural network algorithm in the FD pipeline. This adaptive technique is also effective in improving the robustness of the fault signal estimation. Moreover, support vector machines (SVMs) that can derive adaptive threshold values are used to categorize faults. To design an effective fault-tolerant controller (FC), an adaptive modern fuzzy backstepping variable structure controller is used in this study. First, a new variable structure controller is designed. Next, to increase robustness and reduce high-frequency oscillations in uncertain conditions, a backstepping algorithm is used in parallel with the variable structure controller to design the backstepping variable structure controller. To design an effective hybrid controller, a fuzzy algorithm is integrated into the backstepping variable structure controller to create a fuzzy backstepping variable structure controller. Then, to improve the robustness and reliability of the FC, a neural adaptive. high-order. variable structure observer is applied to the fuzzy backstepping variable structure controller to design a modern fuzzy backstepping variable structure controller. An adaptive algorithm is used to fine-tune the variable structure coefficients and reduce the effect of faults on the robot manipulator. The effectiveness of the selected algorithm is validated using a PUMA robot manipulator. The neural adaptive. high-order variable structure observer improves the average performance for the identification of various faults by about 27% and 29.2%, compared with the neural high-order variable structure observer and variable structure observer, respectively.

A Non-Singular Fast Terminal Sliding Mode Control Based on Third-Order Sliding Mode Observer for a Class of Second-Order Uncertain Nonlinear Systems and its Application to Robot Manipulators

This paper proposes a controller-observer strategy for a class of second-order uncertain nonlinear systems with only available position measurement. The third-order sliding mode observer is first introduced to estimate both velocities and the lumped uncertain terms of system with high accuracy, less chattering, and finite time convergency of estimation errors. Then, the proposed controller-observer strategy is designed based on non-singular fast terminal sliding mode sliding control and proposed observer. Thanks to this combination, the proposed strategy has some superior properties such as high tracking accuracy, chattering phenomenon reduction, robustness against the effects of the lumped uncertain terms, velocity measurement elimination, finite time convergence, and faster reaching sliding motion. Especially, two period times, before and after the convergence of the velocity estimation takes place, are considered. The finite time stability of proposed controller-observer method is proved by using the Lyapunov stability theory. Final, the proposed strategy is applied to robot manipulator system and its effectiveness is verified by simulation results, in which a PUMA560 robot manipulator is employed.

Investigation on the Discretization of a Repetitive Path Planning Scheme for Redundant Robot Manipulators

Repetitive path planning (RPP) plays an important role in the operation of redundant robot manipulators. In this study, we investigate a new discrete-time RPP scheme depicted in the pseudoinverse-type formulation for redundant robot manipulators. Such a scheme is derived from the discretization of the previous continuous-time RPP scheme using a special numerical difference formula. Theoretical analysis shows that the proposed RPP scheme has the property of cube pattern in the end-effector planning accuracy. On the basis of a PUMA560 robot manipulator with different examples, comparative simulation results are presented to demonstrate the effectiveness and superiority of the proposed RPP scheme.

New P-type RMPC Scheme for Redundant Robot Manipulators in Noisy Environment

SUMMARYRepetitive motion planning and control (RMPC) is a significant issue in the research of redundant robot manipulators. Moreover, noise from rounding error, truncation error, and robot uncertainty is an important factor that greatly affects RMPC schemes. In this study, the RMPC of redundant robot manipulators in a noisy environment is investigated. By incorporating the proportional and integral information of the desired path, a new RMPC scheme with pseudoinverse-type (P-type) formulation is proposed. Such a P-type RMPC scheme possesses the suppression of constant and bounded time-varying noises. Comparative simulation results based on a five-link robot manipulator and a PUMA560 robot manipulator are presented to further validate the effectiveness and superiority of the proposed P-type RMPC scheme over the previous one.

Adaptive Fuzzy Control of Puma Robot Manipulator in Task Space with Unknown Dynamic and Uncertain Kinematic

A In this paper, an adaptive direct fuzzy control system is presented to control the robot manipulator in task space. It is assumed that robot system has unknown dynamic and uncertain kinematic. The control system and adaption mechanism are firstly designed for joint space tracking. Then by using inverse Jacobian strategy, it is generalized for task space. After that, to overcome the problem of Jacobian matrix uncertainty, an improved adaptive control system is designed. All the design steps are illustrated by simulations.

Adaptive Fuzzy Control of Puma Robot Manipulator in Task Space with Unknown Dynamic and Uncertain Kinematic

A In this paper, an adaptive direct fuzzy control system is presented to control the robot manipulator in task space. It is assumed that robot system has unknown dynamic and uncertain kinematic. The control system and adaption mechanism are firstly designed for joint space tracking. Then by using inverse Jacobian strategy, it is generalized for task space. After that, to overcome the problem of Jacobian matrix uncertainty, an improved adaptive control system is designed. All the design steps are illustrated by simulations.

General Square-Pattern Discretization Formulas via Second-Order Derivative Elimination for Zeroing Neural Network Illustrated by Future Optimization.

Previous works provide a few effective discretization formulas for zeroing neural network (ZNN), of which the precision is a square pattern. However, those formulas are separately developed via many relatively blind attempts. In this paper, general square-pattern discretization (SPD) formulas are proposed for ZNN via the idea of the second-order derivative elimination. All existing SPD formulas in previous works are included in the framework of the general SPD formulas. The connections and differences of various general formulas are also discussed. Furthermore, the general SPD formulas are used to solve future optimization under linear equality constraints, and the corresponding general discrete ZNN models are proposed. General discrete ZNN models have at least one parameter to adjust, thereby determining their zero stability. Thus, the parameter domains are obtained by restricting zero stability. Finally, numerous comparative numerical experiments, including the motion control of a PUMA560 robot manipulator, are provided to substantiate theoretical results and their superiority to conventional Euler formula.

On the Voltage-based Control of Robot Manipulators Revisited

In this note, we show that the voltage-based control strategy proposed in the paper [1] cannot be applied and act without assumption of uncertainty in the motor parameters. The fact that robots’ actuators have limited voltage is also considered in both control design and stability analysis. It proves that the closed-loop system is stable, while actuator/joint position tracking errors are uniformly ultimately bounded in agreement with Lyapunov’s direct method in any finite region of the state space. Finally, we show that for the PID control structure reported in the aforementioned paper, stability analysis can be guaranteed without any problem. Simulation results on a Puma560 robot manipulator driven by permanent magnet DC motors indicate the efficiency of proposed method.

Two New Discrete-Time Neurodynamic Algorithms Applied to Online Future Matrix Inversion With Nonsingular or Sometimes-Singular Coefficient.

In this paper, a high-precision general discretization formula using six time instants is first proposed to approximate the first-order derivative. Then, such a formula is studied to discretize two continuous-time neurodynamic models, both of which are derived by applying the neurodynamic approaches based on neural networks (i.e., zeroing neurodynamics and gradient neurodynamics). Originating from the general six-instant discretization (6ID) formula, a specific 6ID formula is further presented. Subsequently, two new discrete-time neurodynamic algorithms, i.e., 6ID-type discrete-time zeroing neurodynamic (DTZN) algorithm and 6ID-type discrete-time gradient neurodynamic (DTGN) algorithm, are proposed and investigated for online future matrix inversion (OFMI). In addition to analyzing the usual nonsingular situation of the coefficient, this paper investigates the sometimes-singular situation of the coefficient for OFMI. Finally, two illustrative numerical examples, including an application to the inverse-kinematic control of a PUMA560 robot manipulator, are provided to show respective characteristics and advantages of the proposed 6ID-type DTZN and DTGN algorithms for OFMI in different situations, where the coefficient matrix to be inverted is always-nonsingular or sometimes-singular during time evolution.

FPGA-based ARX-Laguerre PIO fault diagnosis in robot manipulator

The main contribution of this work is the design of a field programmable gate array (FPGA) based ARX-Laguerre proportional-integral observation (PIO) system for fault detection and identification (FDI) in a multi-input, multi-output (MIMO) nonlinear uncertain dynamical robot manipulators. An ARX-Laguerre method was used in this study to dynamic modeling the robot manipulator in the presence of uncertainty and disturbance. To address the challenges of robustness, fault detection, isolation, and estimation the proposed FPGA-based PI observer was applied to the ARX-Laguerre robot model. The effectiveness and accuracy of FPGA based ARX-Laguerre PIO was tested by first three degrees of the freedom PUMA robot manipulator, yielding 6.3%, 10.73%, and 4.23%, average performance improvement for three types of faults (e.g., actuator fault, sensor faults, and composite fault), respectively.

Bi-criteria minimization with MWVN–INAM type for motion planning and control of redundant robot manipulators

SUMMARYThis study proposes and investigates a new type of bi-criteria minimization (BCM) for the motion planning and control of redundant robot manipulators to address the discontinuity problem in the infinity-norm acceleration minimization (INAM) scheme and to guarantee the final joint velocity of motion to be approximate to zero. This new type is based on the combination of minimum weighted velocity norm (MWVN) and INAM criteria, and thus is called the MWVN–INAM–BCM scheme. In formulating such a scheme, joint-angle, joint-velocity, and joint-acceleration limits are incorporated. The proposed MWVN–INAM–BCM scheme is reformulated as a quadratic programming problem solved at the joint-acceleration level. Simulation results based on the PUMA560 robot manipulator validate the efficacy and applicability of the proposed MWVN–INAM–BCM scheme in robotic redundancy resolution. In addition, the physical realizability of the proposed scheme is verified in practical application based on a six-link planar robot manipulator.